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Build a Course FAQ Agent with MCP, PydanticAI and OpenAI

September 1, 2025, 02:00 Europe/Berlin
Alexey Grigorev
ai-agents llm-engineering agent-systems tooling-architecture mcp rag

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We build a course FAQ assistant from the bottom up. First we expose a plain Python search(query) function to the OpenAI Responses API. Then we turn the same idea into a reusable agent loop and compare toyaikit, OpenAI Agents SDK, and PydanticAI. Finally we move the FAQ tools behind an MCP server. From there a notebook, PydanticAI, Cursor, and VS Code can all reach them.

Links

The main resources:

The system you will build

The final setup looks like this:

flowchart LR NOTEBOOK["Jupyter notebook"] OPENAI["OpenAI Responses API"] FRAMEWORKS["Agents SDK PydanticAI"] MCPCLIENT["MCP clients toyaikit, PydanticAI, Cursor"] MCPSERVER["FastMCP server SSE or stdio"] TOOLS["FAQ tools search, add_entry"] INDEX["minsearch index FAQ JSON"] NOTEBOOK -->|function calling| OPENAI NOTEBOOK --> FRAMEWORKS FRAMEWORKS -->|tool calls| TOOLS NOTEBOOK -->|MCP client| MCPCLIENT MCPCLIENT -->|MCP protocol| MCPSERVER MCPSERVER --> TOOLS TOOLS --> INDEX

The FAQ data comes from the Data Engineering Zoomcamp FAQ. The first half of the workshop keeps the tools inside the notebook so you can see the agent loop directly. The second half moves the same tools into mcp_faq/, which makes them reusable by any MCP client.

Hosted by

Alexey Grigorev

Alexey Grigorev

Chief Agent Officer at AI Shipping Labs

Software engineer and machine learning practitioner with 15+ years of experience building production ML systems. I focus on practical, production-grade ML and AI systems, from early prototypes to reliable systems in production.

I'm the founder of DataTalks.Club, a free community that connects tens of thousands of practitioners worldwide, and the creator of the Zoomcamp series, free, code-first programs that have reached 100,000+ learners globally.

At AI Shipping Labs, I'm building the kind of environment that would have accelerated my own career growth. After years of teaching at scale, I wanted something more focused: a space for action-oriented builders who want to turn AI ideas into real projects. The community gives members the structure, accountability, and peer support to ship practical AI products consistently, even alongside their main jobs.

alexey@aishippinglabs.com

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